Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch

• URL: http://arxiv.org/abs/2110.03149v1
• Date: Thu, 7 Oct 2021 02:46:21 GMT
• Title: Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch
• Authors: Akriti Verma, Valeh Moghaddam and Adnan Anwar
• Abstract summary: We propose an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) This work proposes a two-step user verification algorithm that verifies the user's identity using motion-based biometrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to change in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user's identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results which show how the rate of misclassification drops while using this model with adversarial data.

Related papers

• Support Vector Machine for Person Classification Using the EEG Signals [0.4419843514606336]
  We propose using Electroencephalogram (EEG) signals for individual identification to address this challenge. EEG signals offer promising authentication potential and provide a novel means for liveness detection, thereby mitigating spoofing attacks. This study employs a public dataset initially compiled for fatigue analysis, featuring EEG data from 12 subjects recorded via an eight-channel OpenBCI helmet.
  arXiv  Detail & Related papers  (2024-11-26T14:03:58Z)
• Biometrics Employing Neural Network [0.0]
  Fingerprints, iris and retina patterns, facial recognition, hand shapes, palm prints, and voice recognition are frequently used forms of biometrics. For systems to be effective and widely accepted, the error rate in recognition and verification must approach zero. Artificial Neural Networks, which simulate the human brain's operations, present themselves as a promising approach.
  arXiv  Detail & Related papers  (2024-02-01T03:59:04Z)
• Multi-Channel Time-Series Person and Soft-Biometric Identification [65.83256210066787]
  This work investigates person and soft-biometrics identification from recordings of humans performing different activities using deep architectures. We evaluate the method on four datasets of multi-channel time-series human activity recognition (HAR) Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.
  arXiv  Detail & Related papers  (2023-04-04T07:24:51Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• Motion ID: Human Authentication Approach [0.0]
  We introduce a novel approach to user authentication called Motion ID. The method employs motion sensing provided by inertial measurement units (IMUs) to verify the persons identity via short time series of IMU data captured by the mobile device.
  arXiv  Detail & Related papers  (2023-01-25T09:08:33Z)
• Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation [63.05890836038913]
  We study the role of soft biometrics to enhance person recognition systems in unconstrained scenarios. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems. Experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning.
  arXiv  Detail & Related papers  (2022-10-24T11:29:57Z)
• Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning [0.0]
  This study contributes to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement. We evaluate the performance of different single-modal and multi-modal combinations of the two biometrics.
  arXiv  Detail & Related papers  (2022-05-07T05:16:34Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms [58.156733807470395]
  This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign. The campaign targeted the application of physical access control in a medium-size establishment with some 500 persons. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms.
  arXiv  Detail & Related papers  (2021-11-17T13:39:48Z)
• Biometrics: Trust, but Verify [49.9641823975828]
  Biometric recognition has exploded into a plethora of different applications around the globe. There are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems.
  arXiv  Detail & Related papers  (2021-05-14T03:07:25Z)
• Analyze and Development System with Multiple Biometric Identification [0.0]
  Methods developed earlier to ensure personal the information from the thefts was not effective and safe. Biometrics were introduced when it was needed technology for more efficient security of personal information. In biometric authentication system, user may not remember any passwords or carry any keys.
  arXiv  Detail & Related papers  (2020-04-10T05:08:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.